Generative artificial intelligence (Generative AI), and in particular Large
Language Models (LLMs) have gained significant popularity among researchers and
industrial communities, paving the way for integrating LLMs in different
domains, such as robotics, telecom, and healthcare. In this paper, we study the
intersection of game theory and generative artificial intelligence, focusing on
the capabilities of LLMs to find the Nash equilibrium in games with a mixed
strategy Nash equilibrium and no pure strategy Nash equilibrium (that we denote
mixed strategy Nash equilibrium games). The study reveals a significant
enhancement in the performance of LLMs when they are equipped with the
possibility to run code and are provided with a specific prompt to incentivize
them to do so. However, our research also highlights the limitations of LLMs
when the randomization strategy of the game is not easy to deduce. It is
evident that while LLMs exhibit remarkable proficiency in well-known standard
games, their performance dwindles when faced with slight modifications of the
same games. This paper aims to contribute to the growing body of knowledge on
the intersection of game theory and generative artificial intelligence while
providing valuable insights into LLMs strengths and weaknesses. It also
underscores the need for further research to overcome the limitations of LLMs,
particularly in dealing with even slightly more complex scenarios, to harness
their full potential.

本研究旨在研究博弈论和生成人工智能的交叉领域，聚焦于大型语言模型在找到混合策略 Nash 均衡的游戏中的能力，发现了大型语言模型在运行代码和提供特定提示的情况下性能显著提高，但也揭示了在难以推断游戏的随机化策略时，大型语言模型的限制性。该论文致力于为博弈论和生成人工智能的交叉领域增添研究成果，同时提供有关大型语言模型优势和劣势的有价值洞察，并强调了进一步研究的必要性以克服大型语言模型的局限，尤其是在处理稍复杂情境时，以发挥其全部潜力。

大型语言模型在混合策略纳什均衡游戏中的应用

Large Language Models Playing Mixed Strategy Nash Equilibrium Games

The fine-tuning of Large Language Models (LLMs) specialized in code
generation has seen notable advancements through the use of open-domain coding
queries. Despite the successes, existing methodologies like
\textit{Evol-Instruct} encounter performance limitations, impeding further
enhancements in code generation tasks. This paper examines the constraints of
existing prompt evolution techniques and introduces a novel approach,
\textit{Instruction Fusion} (IF). IF innovatively combines two distinct prompts
through a hybridization process, thereby enhancing the evolution of training
prompts for code LLMs. Our experimental results reveal that the proposed novel
method effectively addresses the shortcomings of prior methods, significantly
improving the performance of Code LLMs across five code generation benchmarks,
namely HumanEval, HumanEval+, MBPP, MBPP+ and MultiPL-E, which underscore the
effectiveness of \textit{Instruction Fusion} in advancing the capabilities of
LLMs in code generation.

本文研究现有的代码生成方法的性能限制，并引入了一种新的方法 Instruction Fusion（IF），该方法通过混合两个不同的提示来增强代码 LLM 的训练提示的演化，实验结果显示该方法显著改善了代码 LLM 在五个代码生成基准测试中的性能。

指令融合：通过混合方法推进提示演进

Instruction Fusion: Advancing Prompt Evolution through Hybridization

Despite the widespread adoption of face recognition technology around the
world, and its remarkable performance on current benchmarks, there are still
several challenges that must be covered in more detail. This paper offers an
overview of the Face Recognition Challenge in the Era of Synthetic Data
(FRCSyn) organized at WACV 2024. This is the first international challenge
aiming to explore the use of synthetic data in face recognition to address
existing limitations in the technology. Specifically, the FRCSyn Challenge
targets concerns related to data privacy issues, demographic biases,
generalization to unseen scenarios, and performance limitations in challenging
scenarios, including significant age disparities between enrollment and
testing, pose variations, and occlusions. The results achieved in the FRCSyn
Challenge, together with the proposed benchmark, contribute significantly to
the application of synthetic data to improve face recognition technology.

本论文概述了 2024 年 WACV 组织的 “合成数据时代的人脸识别挑战”（FRCSyn）的概况，该国际挑战旨在探索使用合成数据来解决人脸识别技术中的现有限制，包括数据隐私问题、人口偏见、未知场景的泛化能力和具有挑战性场景中的性能限制。FRCSyn 挑战的结果以及提出的基准显著地促进了合成数据在改进人脸识别技术中的应用。